The BNRC has no tendency towards closure at all.

the experiments require two bodies of data: the collection of data that is being examined for fit to the sublanguage model, and a "background" corpus consisting of material in the general (i.e.


Computing BLEU scores over character sequences does not make much sense, especially for small n-gram sizes (usually, n ≤ 4).

pivot languages can also be used for paraphrasing and lexical adaptation (Bannard and Callison-Burch, 2005;Crego et al., 2010).


One possibility is to just apply the models tuned for the individual translation tasks, which is suboptimal.

in future work, we plan to try the more sophisticated bitext combinations from (Nakov and Ng, 2009).


We provided with a qualitative analysis of some of the highest ranking conjunctions and they appear to be strong predictors for Location, that a domain expert would also consider adding to the model.

a larger l opt would mean that the model benefits from conjunctions between two low scoring atomic features, which is not the case, according to our results.


1 The corpus comprises 1,808 summaries, produced by 452 summarisers, for four different dialogues (each summariser produced a summary for each dialogue).

abundant, such sources have the drawback of being quite generic, making it harder for the researcher to control different phenomena.


Comparing different translation models containing MT useful data ranging from comparable, through strongly comparable, to parallel, we concluded that there is sufficient empirical evidence not to dismiss sentence pairs that are not fully parallel on the suspicion that because of the inherent noise they might be detrimental to the translation quality.

2010, addressed large-scale parallel sentence mining from Wikipedia.


Many documents in one language are shortened or adapted translations 1 of documents from other (not always the same) languages and this property of Wikipedia together with its size makes it the ideal candidate of a strongly comparable corpus from which parallel sentences can be mined.

depending on the comparability level of the extraction corpus, the quantity of parallel data extracted may range from 0.1% (weakly comparable corpora) to 29% (strongly comparable corpora) of the entire corpus (Ion et al., 2011).


In the following figure, we detail the three stages of the proposed approach.

this approach takes advantage of the LMF fine structure that highlights all kinds of relationships between entries' knowledge and distinguishes the role of each available text such as giving definitions and examples.


Indeed, the example" اَم َم ِ ا ُ َم ا َم او َم َم أ -the boy takes the book" given in figure 6 has a syntactic behavior "verb subject object (VSO)".

it removes the longest suffix and prefix.


Indeed, LMF highlights all kinds of relationships between entries knowledge and distinguishes the role of each available text such as giving definitions and ex-amples.


Recently, data selection as a simple and effective way for this special task has attracted attention.

it needs a large size of the selected subset (more than 50% of general corpus) to obtain an ideal performance.


The classification method has been run on a subset of sentences of Wall Street Journal (WSJ) annotated with the elementary tree of the XTAG grammar.

the annotation process has been done according to the treebank creation method introduced in Basirat and Faili (2013).


The difficulties, raised in the manual creation of Treebanks, led the researchers to use automatic and semi-automatic methods of treebank development methods.

the combination of Adaboost and Random Forest has been used in the traffic flow (Leshem, and Ritov, 2007) and cancer survivability (thongkam et al, 2008) and improve the performance of them.


In any case, the lexical network that is built is not free of errors which are corrected along their discovery.

this confidence is done by comparing the relation weight to a confidence threshold which is computed as the starting point of the long tail in the distribution of the relation.


The concept of a mention is closely related to NPs in syntax.

such complex cases cannot be covered by a simple heuristic, but rather need to be defined via language dependent rules in order to be captured properly across languages.


On average every third sentence contains at least one co-reference chain.

in this way, all information about the potential tags and the correct tag is represented in the form of a subtree, attached to the wordform.


However, the tool is not freely available and is not open source.

as for Hungarian POS-tagging, hunpos was developed on the basis of hunmorph (Halácsy et al., 2006).


In order to avoid this, the two coding systems (MSD and KR) were harmonized and their basic principles were also made compatible.

of the morphological analysis, pairs of lemmas and morphological codes are provided for each word.


which means that most of the suffixes exist in two different forms -one with a front vowel and another one with a back vowel -and it is the vowels within the stem that determine which form of the suffix is attached to the word.

the codes Nc-sd and Nc-sd---s3 will be reduced to the same form since there is no such Hungarian lemma that would have the same word form for a dative singular and a dative singular with a third person singular possessor (and thus, the POS-tagger would not have to choose between these possibilities).


Concatenation In this classification, two or more words were connected to each other to form one token.


Then, we will retrain Stanford tagger on Arabic tweets since its speed is ideal for tweets domain and it is only the retrainable tagger.

we used Twitter Stream API to crawl Twitter by setting a query to retrieve tweets from the Arabian Peninsula and Egypt by using latitude and longitude coordinates of these regions since Arabic dialects in these regions share similar characteristics and they are the closest Arabic dialects to MSA.


Also, accuracy calculated in this manner is not ideal and does not provide a reliable result since each calculation only factors in true positives and true negatives per class.

in this case, we only focused on accuracy, starting with the userbased evaluation as the authoritative score.


In addition we also intend to improve the grammar rules to cover more of the exceptions and characteristics of the Persian language.

based on this comparison, we calculated two measures: 1.


Due to the limitations discussed above, we performed a second evaluation.

in this second phase the JAPE rules follow this sequence: 1.


It may also be caused by the absance of high granularity features of our dictionary entries.

on the other side, the representation of terminological data in a standard format allows the integration and merging of terminological data from multiple source systems, while improving terminological data quality and maintaining maximum interoperability between different applications.


Now the essence of the EM algorithm (Algorithm 1) can be described as follows.

observing a small value of W /(M + N ) is one of the indicators in favor of parallelism of two pages.


Although the idea of receiving a direct response to an information need sounds very appealing, CQA websites also involve risk as the quality of the provided information is not guaranteed.

we test the influence of question tags, length of the question title and body, presence of a code snippet and the user reputation on question quality.


SENTIPOLC dataset is made of a collection of Tweet IDs, since the privacy policy of Twitter does not allow to share the text of Tweets.

in order to compare our system with the best ones of SENTiPOLC, beside using the same dataset, we adopted the same experimental framework.


The domain dependent group includes Word-Based and Synsets features described in Section 4.1 and 4.2 often used in text classifications and topic recognition tasks.

detecting if the Tweet is subjective before deciding if it is ironic, as irony implies subjectivity).


In order to compare our system with the best ones of SENTIPOLC, beside using the same dataset, we adopted the same experimental framework.

in the SENTIPOLC task domain dependent features were relevant, and detecting the topic of a specific class was important.


The corpus of study was collected from Arabic Wikipedia through Arabic kiwix 1 tool.

this system consists of a tokenizer, a morphological analyzer and a NE finder.


The second dictionary contains the last names.

figure 7, the transducer treat exceptional cases in the corpus of study.


we find that the results for the proposed method are motivating.


Finally, we also wished to accommodate systematical errors made by immigrants or foreign language learners in Denmark, in particular endings errors due to category confusions 5 (e.g.

we therefore modified DanGram's analysis module to recognize and mark this kind of error.


This problem is often expressed as the ultimate objective, finding the author.

the corpus main characteristics are drawn in table 2  LIB contains the same number of authors as EBG, but the number of texts bounded to each author is higher (31.2 ± 4.2 texts per author in LIB, 15.8 ± 2.6 in EBG).


Event Detection (ED), one aspect of Information Extraction, involves identifying instances of specified types of events in text.

even if this pattern did not appear in the training data, adding it during pattern expansion will do little to improve event classifier accuracy because there are many Die events in the training data whose trigger is the verb "die".


Much of the research on ED has been based on the specifications of the 2005 ACE [Automatic Content Extraction] event task 1 , and the associated annotated corpus.

regarding the correctness criteria, following the previous work (Ji and Grishman, 2008;Liao and Grishman, 2010;Ji and Grishman, 2011;Li et al., 2013), a trigger candidate is counted as correct if its event subtype and offsets match those of a reference trigger.


(2013) implements a joint model via structured prediction with crossevent features.

most words have multiple senses and so may be associated with multiple types of events.


From a practical point of view in software application (real scenario) for the algorithms we also do not have the assurance that all documents given as examples of an author, have actually been written by the author in question.

one of the principal evaluation labs for the dissemination, experimentation and collaboration in the development of methods for the authorship analysis is found in the PAN 1 lab associated to CLEF.


In Table 3 we report the precision, recall and F-score for the prediction task.

it assesses the level of readability accounting for the number of syllables per word (as an approximation of the difficulty of a word) and for the number of words per sentence (as estimation of the syntactic difficulty of a text).


We extract randomly from our dataset 1,000 English original sentences and 1,000 sentences translated into English 3 .

we conclude that readability features cannot discriminate between original texts and translations significantly better for some of the source languages than for the others.


Brown clustering (Brown et al., 1992) uses distributional information to group similar words.

for the social media data (SM), we observe unstable quality for large |T | (Table 3).


Datasets We evaluate Brown tuning using two text types.

we introduce an <EOS> marker after each tweet to break bigrams.


Our findings suggest that a number of factors other than content coverage are important to consider when it comes to summarizing opinions from social media.

part-of-speech based grammatical features have been widely used in text quality prediction (Feng et al., 2010;Dell'Orletta et al., 2014).


the Food category includes Dishes (pudding), but also Animals, Vegetables, Insects, Fruits, etc.

proper nouns and multi-word expressions were filtered out, as the techniques presented in this paper target singleword common nouns.


This work is the first to use semantic preferences from PDEV for ontology population from the web, therefore it is still work in progress.

for each ST and each verb unambiguously taking the ST as subject/object, all verb occurrences were extracted together with their direct syntactic dependents, as well as dependents indirectly connected to the verb via coordination with a direct dependent.


For these 2,000 affectations, the expert approves that 232 incorrect affectations and 140 missed ones are detected.

at the end of this step, for each processed Context the syntactic behaviour is identified.


For instance, prior experiments on the WCL dataset showed results ranging from F=54.42 to F=75.16 Boella et al., 2014).

in relation to this, we also performed experiments with a development set automatically constructed from the Web, but due to lack of preprocessing for noise filtering, results were unsatisfactory and therefore unreported in this paper.


It has received notorious attention for its potential application to glossary generation (Muresan and Klavans, 2002;Park et al., 2002), terminological databases (Nakamura and Nagao, 1988), question answering systems (Saggion and Gaizauskas, 2004;Cui et al., 2005), for supporting terminological applications (Meyer, 2001;Sierra et al., 2006), e-learning (Westerhout and Monachesi, 2007), and more recently for multilingual paraphrase extraction (Yan et al., 2013), ontology learning (Velardi et al., 2013) or hypernym discovery (Flati et al., 2014).


A feature targeting the context of a mention's head to model selectional preference.

the Berkeley System expands the feature space by feature conjunctions: If a pairwise feature f fires for current mention m c and antecedent mention m a , features f ∧ type(c) and f ∧ type(c) ∧ type(a) are also activated, where type(•) returns a mention type literal based on the head's POS.


We use index k to range over multiple relations between the nodes of a parse tree for a pair of sentences.

this discourse structure needs to include anaphora, rhetoric relations, and interaction scenarios by means of communicative language (Galitsky and Kuznetsov, 2008).


Since important phrases can be distributed through different sentences, one needs a sentence boundaryindependent way of extracting both syntactic and discourse features.

the one of the tree kernel based methods improves as the sources of linguistic properties are expanded.


Intuitively, we would like the training algorithm to fit the vectors so that v c (c) is a high number if we are likely to see c near w, and a low number otherwise.

when the algorithm encounters a word w, it first represents the full context window by building a sumv c of the context vectors of the words appearing in the window.


Although the lack of context to describe the actual usage of word makes them unsuitable for word sense evaluation, they have been used to evaluate sense-aware vector-space models (Reisinger and Mooney, 2010;Neelakantan et al., 2014), so we include a comparison for completeness.

, w t+Rt } to create a combined context: C = C t ∪ C sg .


In all 291 cases две (meaning 'two' for feminine and neuter gender) was annotated as Mc-pi.

the corrections made to the corpus are linguistically motivated and linguistically motivated corrections are needed for further progress (Manning, 2011).


There have been several efforts on social media text POS tagging in recent years, but almost exclusively on Twitter and mostly for English (Darling et al., 2012;Owoputi et al., 2013;Derczynski et al., 2013) and German (Rehbein, 2013;Neunerdt et al., 2014).

when working with Facebook messages, we found several long posts, with a high number of code alternation points (6-8 alternation points are very common).


In addition to preparing a dataset of annotated tweets, we further focus on creating sentiment polarity lexicons for Macedonian.

in both tasks, the winning systems benefited from building and using massive sentiment polarity lexicons (Mohammad et al., 2013;.


Turney's PMI-based approach further serves as the basis for two popular large-scale automatic lexicons for English sentiment analysis in Twitter, initially developed by NRC for their participation in SemEval-2013 (Mohammad et al., 2013).

we developed a corpus of tweets annotated with tweet-level sentiment polarity (positive, negative, and neutral), as well as with phrase-level sentiment, which we made freely available for research purposes.


4 shows that the ATP assigned (correctly) keywords with negative tonality only to 57.

the semantic information, which is saved up in the metadata, enables the development of various searching strategies that rely significantly on automatic text processing, lexical hierarchies and information search techniques.


At this point, we can only note that we used SVM as the basic underlying classifier in our classification and regression experiments, but we used logistic regression as the basis for our ordinal regression.

we can see in Table 2 that the best results are achieved for regression, where the mean squared error is within half a point away for the 11-way and the 5-way class inventories, and it is about four times lower for the 3-way one.


As the small amount of available training data limits the development of robust systems to automatically detect comparisons, this paper investigates how to use semi-supervised strategies to expand a small set of labeled sentences.

as the unlabeled expansion data, we use a set of 280.000 camera review sentences from epinions.com.


Benthem (1983) uses interval as something that is between boundaries.

recognition and classification of temporal expressions; available classes: DATE, TIME, DURA-TION and SET) and 1-class (boundaries recognition only, all classes cast to a single class called timex).


We can see that adding new features improved F 1 for each model and for each match evaluation.

allen (1995) finds interval temporal expressions in Benthem's meaning denoted by the term duration.


We examined to see if the existing corpora were useful with the corpus that we built using domain adaptation and showed that the filtering of the source data assisted in the domain adaptation work.

it is difficult to use these types of methods in our system because the anime-related NEs such as the titles of an anime often do not match the patterns.


We consider our system to be a part of the Closed sub-task, which is the 11-way classification task using only the TOEFL11 data for training.

in combination with other families, it finally appeared that the best performance was achieved with the suffixes with the length of 4, which was found using the cross-validation on the training data set.


It may be relevant to assign to words some information in the form of finite sets of values.

roughly speaking, in the context of sentiment analysis of a given text, combining polarizations of contained words means adding such normed vectors.


The medical computer systems allow to archive many and varied information (for example medical record, results of medical analyses, X-rays and radiological reports…).

although this network is general, it contains many specialty data, including medicine/radiology, which we have added within the framework of IMaIOS project.


(2009) seem to contradict this hypothesis showing that corpus-based approaches can be as good at identifying similarity (when the right model is based on enough data).

the distance in number of nodes) from s 1 to s 2 plus one.


We are grateful to the anonymous reviewers for their valuable feedback on an earlier version of this paper.

the combinators contain the functional application, coordination, composition, type-raising and type-shifting.


For GeoQuery, they include the ZC07 (Zettlemoyer and Collins, 2007), λ-WASP (Wong and Mooney, 2007), UBL (Kwiatkowski et al., 2010)  FUBL (Kwiatkowski et al., 2011).

ccG is a linguistic formalism that tightly couples syntax and semantic (Steedman, 1996;Steedman, 2000).


The Geo-Query database contains only a single geography domain, 7 relations, and 698 total instances.

this algorithm is online, repeatedly performing both lexical expansion (Step 1) and parameter update (Step 2) procedures for each training example.


Non-standard linguistic features have been analysed both qualitatively and quantitatively (Eisenstein, 2013;Hu et al., 2013;Baldwin et al., 2013) and they have been taken into account in automatic text processing applications, which either strive to normalise non-standard features before submitting them to standard text processing tools (Han et al., 2012), adapt standard processing tools to work on non-standard data (Gimpel et al., 2011) or, in task-oriented applications, use a series of simple pre-processing steps to tackle the most frequent UGC-specific phenomena (Foster et al., 2011).

the learning of the technical and linguistic dimensions seem to be equally hard when there are up to 100 instances available for learning, the technical dimension taking off after that.


The first source consists of a simple match among the lemmas in T 1 and T 2: if two lemmas are equal (case insensitive), then we count it as an alignment between T 1 and T 2.

the goal is to predict the behavior of an entailment algorithm given the characteristics of both the resource and the data-set.


For example, if a sequence of tokens T in one sentence has labelling L, then there is high probability that the same sequence in an another sentence will have the same labelling.

one direction is to decompose the task into two stages: named entity boundary detection and classification .


The advantage of CRFs over other statistical models (like Hidden Markov Models) is that they can utilize a large set of features describing a sequence of observations.

this feature checks the number, case and gender agreement between adjacent tokens.


We then started from a more general set L1, but good trees were hard to obtain.

this type is the most common in plWordNet (almost 50% of multiword lexical unit instances -see Figure 3).


the most prominent theory in Computational Linguistics to structure the discourse of a text is the Rhetorical Structure theory (RSt) proposed by Mann and thompson (1987).


The resulting probabilistic model is sparse and many equal instances may indicate different relations (classes).

also, given that the number of features will increase, feature selection may be applied to select the most informative features in each iteration of the SSNEL.


(3 -Avg comments per publication) shows that both paid and "mentioned" trolls post more comments per publication than non-trolls.

we also plan to analyze the comment threads as a whole .


It is interesting to see how our classifier would perform if tested on trolls with different minimum number of comments (and the corresponding number of non-trolls).

this is much of a witch hunt and despite our good overall results, the training data is not 100% reliable.


The drawback of this method is that phrases are built in an iterative process starting from single words and joining others to them until the expected size of phrases is reached.

• To take into account long distance dependency: the phrase-based language model can easily capture the long distance relationship between the different components of the sentence.


5: Execute 2, 3 and 4 but switch the source and the target corpora.

word-based language model is a special case of phrase-based language model if only single word phrases are considered.


In a related example, Figure 1 shows the projections onto a 2D space 3 of the representations for the two senses ofåsna: 'donkey' or 'slow-witted person', and those of their corresponding nearest neighbors.

formally: our training objective is to maximize the sum of the log-probabilities of context words c given a sense s of the target word w plus the logprobability of the sense s given the target word: We must now consider two distinct vocabularies: V containing all possible word forms (context and target words), and S containing all possible senses for the words in V , with sizes |V | and |S|, resp.


In these cases we evaluate our result as incorrect.

in this paper we explain the methodology and results of the experiments for the enlargement of the Croatian Wordnet using the WN-Toolkit and the derivational database CroDeriV.


We have used a state-of-theart word sense algorithm (Freeling+UKB), so we don't expect to make any improvement in the tagger.

this method significantly contributed to the improvement of the CroWN's coverage of lemmas from various Croatian corpora.


Negation and modality triggers are identified and their scope is determined (Rosenberg et al., 2012) in order to extract the contextaware sentiment association values 4 with PMI for unigrams, bigrams, and dependency triples (typegovernor-dependent).

for a term to fall into the positive categories, it has to occur at least twice as often in positive tweets as in negative tweets, thus positive terms have association scores greater than 1.


Tweets use more informal and non-standard language than other text forms posing additional challenges.

negation does not always reverse the effects of the sentiment carriers, as the case of judgements illustrates: This isn't awful.


Explicit attributes are the attributes mentioned by the user in the NL query text.

(eg., the word each may trigger GROUP BY clause).


We used Conditional Random Fields (Lafferty et al., 2001) for the machine learning task.

the attribute professor name is an implicit attribute.


In this representation, each term is a product of p and typical tf-idf.

the list contains only those VNCs whose frequency was greater than 20 and that occurred at least in one of two idiom dictionaries (Cowie et al., 1983;Seaton and Macaulay, 2002).


For our experiments we only use VNCs that are annotated as I or L. We only experimented with idioms that can have both literal and idiomatic interpretations.

this 200 dimensional vector space provides a basis for our experiments.


A word can be represented by a vector of fixed dimensionality q that best predicts its surrounding words in a sentence or a document (Mikolov et al., 2013a;Mikolov et al., 2013b).

the words associated with a literal target will have larger projection onto a target σ vn .


For instance, linguists rejected such word combinations as koszt zakupu 'the cost of buying something' or kolor włosów 'hair coloring', while accepted płaca minimalna 'minimum wage' or ośrodek zdrowia 'health centre'.

results of experiments are presented in Table 1.


POS bi-gram feature set is superior among the other n-grams' (i.e.

for CI, we solve the equation , where q * is the reduced dimensionality matrix representation of the original training, reference or test matrix.


This is because 'extended substitution' is, on the one side, the most common type of error (such that sufficient training material is available), and, on the other side, very variant (such that it is difficult to be captured by a rule-based procedure).

firstly, a certain number of collocations was affected by spelling and inflective errors.


The lexical features consist of the lemma of the collocate and the bigram made up of the lemmas of the base and collocate.

if this is not the case, it checks whether the gender suffix is wrong, considering it as a 'gender Creation error', as in, e.g., *hacer regala instead of hacer regalo, lit.


In such cases, we assume that these are orthographical or morphological mistakes, rather than collocational ones.

the prototypical context is represented by a centroid vector calculated using the lexical contexts of the correct uses of the collocation found in the RC.


(2007) have shown, it is useful to take such adverbial intensification into account when predicting document-level sentiment scores.

(2014) on ordering adjectives, maximizers and approximators were grouped as one pole of attraction for adjectives, and boosters, moderators, and diminishers as another.


Our solution to this problem involves increasing the language model training corpus from 22M to 47M sentences, while leaving the vocabulary size at 200K units.

implementing such a converter for Latvian is challenging, because all word endings must be matched.


For example there are results for English (Fosler-Lussier & Morgan, 1999) and Japanese (Shinozaki & Furui, 2001) that show that infrequent words are more likely to be misrecognized, which is most likely to be true also for other languages.

in order to improve ASR performance, it is important to understand which factors are most problematic for recognition, identify the types of errors, their main causes and how critical these errors are.


Section 5 describes and evaluates the algorithm to build the hierarchy of topically focused fragments.

burst analysis is relevant in the context of hierarchical topic segmentation, but an appropriate way to exploit it has to be proposed; we address this open issue in the following section.


We estimated "prolificness" of the authors as the ratio of the total number of author"s posts to the total number of posts of the most prolific author in the studied topics (Patil et al, 2013).

our current work studies relations between authors" activity levels and expressed sentiments in an online IVF forum.


paraphrase, summarisation) which cannot be modelled by standard PB-SMT systems.

we focused on detailed analysis of the generated sentences in all three languages, seeking to discover what are the possibilities and limitations of our simplification models.


2 We created Czech dictionaries and a cascaded grammar for analysis of crisis events, as well as boolean combination of keywords for recognition of event types, which was then used in the multilingual event extraction system -NEXUS (Tanev et al., 2008).

the dictionaries used by NEXUS are developed following a semi-automatic procedure described in (tanev et al., 2009).


There has been an increased interest in discovering the semantics of Noun Compounds (NCs 1 ).

the experiments on RELAtION Nastase (2003) (Beamer et al., 2008;turney, 2006b).


The model employs a Best-Selection strategy which does nothing more than selecting the more suitable model for classification for any given test instance.

this experiment investigates the relevance of verb+prep paraphrases (e.g.


One problem that we have not tackled yet is determining the language of origin for each name.

the second step was to apply the first models on the data to further clean and tidy it up (details are given in section 5.1), so that a second, better series of models is obtained.


The discrepancies between the two human transliterations demonstrate the complexity of the task.

there are often multiple transliterations of the same name.


This actually happens because the English word "or" is always related to the Arabic word .

these words must be recombined into their surface forms.


Hence, having assumed that paraphrased pairs would share the same content and similar syntactic structures, we decide to choose the Microsoft Research Paraphrasing Corpus (Dolan et al., 2005) which contains 5,800 sentence pairs extracted from news sources on the web, along with human annotations indicating whether each pair captures a paraphrase/semantic equivalence relationship.

compared to the baseline, the contribution of syntactic structure is not significant to the overall performance.


One limitation is that the metaphor index needs to be derived manually and that manual annotation of metaphors can be an onerous and unreliable process.

index (6), the only index whose evaluation requires human input, estimates the difficulty posed by texts to autistic readers due to their difficulties in understanding metaphor and figurative language.


They utilise the LCD algorithm that tests variables for dependence, independence, and conditional independence to restrict the possible causal relations (Cooper, 1997).

we search for causal relations between events without considering the object, between events given the object, and between events and states.


We have adopted a rich annotation model in which sentiment polarity description is combined with emotion categories.

automatic annotation of lexical material is not a viable option.


This aspect creates semantic ambiguity in which one word could imply different meanings.

this same set of tweets was used to evaluate the proposed approach and the result is presented in Section 5.


Parsing linguistic expressions (e.g., noun phrases (NPs)) is a fundamental component in many natural language processing (NLP) tasks like machine translation (MT) or information retrieval (IR) and indispensable for understanding the meaning of complex units.

to FAST, SAST produces a more fine-grained scoring by exploiting more data.


The task, as well as approaches proposed for determining tweet level sentiment, are nicely summarized in the survey paper of Kharde et al.

overall we see that the performances of the classifiers are all highly satisfactory.


For example in Twitter, tweets may report about news related to recent events such as natural or man-made disasters, discoveries made, local or global election outcomes, health reports, financial updates, etc.

while defining news types, topicality plays an important role.


By utilizing the roundtripping approach, the monolingual data play a similar role to the bilingual data, effectively reducing the requirement for parallel data.

if more than two associated tasks form a closed loop, this approach can applied in each task for improvement of the overall model, even in the face of unlabeled data.


3 From SMT to PBMST Our enhancement to SMT takes a noisy channel model as a starting point, where translation is modeled by decoding a source text, thereby eliminating the noise (e.g., adjusting lexical and syntactic divergences) to uncover the intended translation.

4 For our purposes, we have trained the Spanish LM using about 700M words of crawled newspaper material.


After analyzing Turkish sentences we found out some sentences have more than one predicate, so we continued to search for another predicate in the sentence and ran the same procedure for each predicate candidate.

these 5,999 annotations may be hand-annotated and recompared for validity of the transferred annotations.


It has been applied to more than 15 different languages.

process only requires a source language model and parallel data to construct target SRL model.


This approach increased F1 scores 1.52 and 1.74 respectively for Chinese and English.

when we look at the all word results, 12,966 roles were not transferred.


In Figure 1, sample morphological structure is displayed.


We can say these semi-space signs segment words into smaller morphemes.

as is shown in Table 1, our Bi-LSTM model performs slightly better than the rest in boundary prediction, however, the classification models are surprisingly almost on the par with our complex DL model.


Besides the classification-based segmentation models, we designed and evaluated five DL models based on GRU, LSTM, Bi-LSTM, seq2seq and Bi-LSTM with the attention mechanism, respectively.

in formal writing and in all Persian normal corpora, this space is neglected frequently and it could make a lot of problems in Persian and Arabic morphological segmentation task.


The "de" following "Benim" refers to "also".

to train the model, sentences with both correct and incorrect spellings of the clitic "de/da" are required.


For the spell correction task, vocabulary based methods have been replaced with methods that take morphological and grammar rules into account.

this model uses various word embeddings to train a model for the named entity recognition task for this clitic.


Propaganda of various pressure groups ranging from big economies to ideological blocks is often presented in a form of objective newspaper texts.

the main way of self-protection against such propaganda influence lies in careful verification of the presented information sources.


Their presence in texts may take the form of "indices".

among the achievements, one can distinguish the tools which take into account the difference between the lexical term and the ontology concept (differentiated tools) and those that do not make such distinction.


The scientific community works intensively on data acquisition for the ontology building.

even though we assume the pivot as being interlingual, it is still close to a natural one.


The rule has the following form: property=aPourEtatPhysique (property name) src=?s (domain, set of terms) reltype=r_carac (relation type) tgt=?o(range, set of terms) source_isa=aliment(src hypernyms) target_isa=etat physique (tgt hypernyms) annotation=int:physical state (meta-information) src_feat=OUT/r_pos/int:Noun (in and out relations that characterize terms in the source set) tgt_feat=OUT/r_pos/int:Adj (in and out relations that characterize terms in the target set) If a given rule allowed detecting enough structures in the source language (at least, 2 structures), it is considered as a valid one and can generate the qualifying object.

it can be considered as a union of word senses lexicalized or identified in the languages covered by the MLSN.


The relation typed r carac is annotated.

such approach would be error-prone due to the potential alignment and polysemy issues.


Table 3 shows that our baseline is +1.5 BLEU points better than the scores reported by Luong et al.

it has been shown that transfer learning approaches using out of domain data, such as the European Parliament data 1 , to regularize the learning helps improve the translation quality (Miceli Barone et al., 2017).


This allows the tagger to fill in the gaps with reasonable templates that are in the lexicon.

we then limit our search to the most probable templates as predicted by the supertagger.


As will become clear in what follows, the Encoder of the QBERT architecture is able to compute the hidden representation of a word w t in a sequence w 1:n as a function of the weights and of the whole sequence except w t itself, i.e.

with respect to our own comparison systems, QBERT performs better than ELMo, Flair and SBERT in this setting as well.


CLM is inherently unidirectional, as the model must not be able to "peek" at the word it has to predict.

representations from Transformers), a baseline featuring the same architecture as QBErT but missing the BiTransformer layer: the outputs of the past and future stacks are simply combined through elementwise sum after the position shift.


because it is sampled from the same text).

these works contain many one-letter variable names, logical symbols and other formal language that a model might otherwise use to position vectors of Quine terminology in particular areas of the semantic space, as these tokens are highly infrequent in the general domain.


Given a pre-trained background space kept frozen across experiments, the vector representation of a target is generated by simple vector addition over its context words.

in the full Quine dataset, 68.6% of terms reach the maximum size, while in the Word & Object dataset, only 32.1% of terms reach it.


As we increased the percentage of request goals in the warm start buffer, the overall success rate decreases and the learning curve becomes less smooth.

the effect of pre-training with very large datasets observed in the experiments is the most surprising result of the paper (Erhan et al., 2010): the early examples determine the basin of attraction for the remainder of the training and the supervised fine-tuning cannot escape from it.


The ordeal started february 1 when several police officers and a bailiff went to a home hoping to get payment for a gas bill , said butt , a senior police official in lahore.

the ability to facilitate deeper understanding of texts is an important, but recently ignored, property for coherence modelling approaches.


The diagnosis with ICD-10 code I20 "Ischaemic heart disease" is chosen for experiments because the Diabetes Register contains a plenty of clinical descriptions about this case.

although only the top 5 most related documents are taken into consideration, some of the extracted Wikipedia pages are not directly related to the symptoms and risk factors, as they discuss e.g.


• Co-referencing Features Often discussion about a work or the author's work will be carried out over several sentences.

these original labels are both quite different linguistically and we speculate that this might prove difficult for the classifier.


More frequently occurring categories, cited work description (CW-D), background sentences with and without evidence (BG-NE, BG-EP) are more robust to feature omissions.


An example of stimulus image is illustrated in Figure 2.

we notice that the increasing availability of text corpora labelled with author demographics in general (e.g., gender, age, education information etc.)


Armed with the pseudo-labelled data generated via Algorithm 1, we can now learn a projection for the target domain, T prj , following the same procedure we proposed for learning S prj in Section 3.1.

as stated above, our proposed method Self-adapt, differs from the prior work discussed above in that it (a) does not require pivots, (b) does not require multiple feature views, (c) learns two different projections for the source and target domains and (d) combines a projection and a selftraining step in a non-iterative manner to improve the performance in UDa.


Train a sentiment classifier on S * L ∪ T L and use it to classify target domain test instances.

recent work on UDA (Morerio et al., 2018) has shown that minimising the entropy of a classifier on its predictions in the source and target domains is equivalent to learning a projection space that maximises the correlation between source and target instances.


For this purpose, the union of the source and target feature spaces is split into domain-independent (often referred to as pivots) and domain-specific features using heuristics such as mutual information or frequency of a feature in a domain.

3 Self-Adapt does not require sub-sampling of unlabelled data and uses all the available unlabelled data for UDA.


(The patient will be discharged from the intensive care unit without renal failure after eight days of management and five hemodialysis sessions.)

even though CAS (speculation) was only trained on 226 examples, the model still shows decent results in scope tokens detection.


", which demonstrates the effectiveness of our dependency-based self-attention.

table 3 also shows that the proposed model outperforms the baseline model when BPE is not used.


In both cases, the most important information sources are stance (does a tweet or a news article agree or disagree with the claim?

we further used a variety of domain-specific features, which we eventually combined in a meta classifier.


We tried simple random oversampling as well as the more complex SMOTE (Chawla et al., 2002) model.

they argued that the title and the body should be analyzed separately.


Otherwise, the base system replaces the entities with default entries in the substitute lists, i.e., generally (non-inflected) lemmata.

all sensitive information was replaced with corresponding semantic placeholder codes of the encountered semantic type (e.g., each specific email address was replaced by the type symbol EMaIL), not by an alternative semantic token, i.e., a pseudonym.


For this first attempt of developing an Old Tibetan Treebank, we therefore decided to reduce the amount of tags to a small and simplified version of the standard Universal Dependency POS set, consisting of 15 tags only (De Marneffe et al., 2014).

we substituted 'I' with 'i', which is the standard wylie transliteration for this character, as shown in (1): (1) rgyal po'I > rgyal po'i 'of the king' The Old Tibetan script furthermore presents a set of features that need to be 'normalised' or converted to a form that looks like Classical Tibetan.


Without adding the Classical Tibetan training data, however, the vocabulary list that the memory-based tagger builds would simply be too small to get any good results on unseen data.

in practice, Unicode Tibetan script is far more widely used within the Tibetan community.


Training takes up 60% of the instances (6,373), whereas validation and test have 20% of the instances each (2,125).

summarization results in other datasets are not directly comparable to our results.


In the legal domain, it is common to reference existing laws, specific dates, and names.

there are some peculiarities when using a language different from English, e.g., we need to check if the standard summarization evaluation (designed for English) can be directly applied.


Unlike the handcrafted gold standard, the automatically generated tests were produced randomly by a machine with no knowledge of test design so we would expect automatic gaps to be often inserted in inconvenient locations within the text, yielding lower quality tests.

entropy is shown to provide insights into the expected difficulty of the question and correlate directly with the target proficiency level of the exercises.


In this regard, Skory and Eskenazi (2010) observe that Shannon's information theory (Shannon, 1948) could be used to estimate the reading difficulty of answers to a gap based on their probability of occurrence.

these are referred to as closed cloze questions, since the answer is limited to the alternatives given.


The fitness F it of the segment cluster C ⊆ S is defined through the precision pr of the cluster and the coverage co of the cluster.

to address this task, we apply the following methods from the literature: the popular graph-based summarizer textRank; an adaptation of a topic-based method (topSum).


Extractive summarization methods identify important elements of the text and generate them verbatim (they depend only on extraction of sentences or words from the original text).

section 3 presents the lyrics summarization task and the proposed methods.


These models differ from more traditional recurrent neural networks in different aspects.

the dictionary of 32 terms that we used for the dictionary lookup method consists approximately half of terms that are quite specific to the Rap genre, such as glock, gat, clip (gun-related), thug, beef, gangsta, pimp, blunt (crime and drugs).


Most of the computational historical linguistics approaches rely on the use of lexical items.

(Franzoi and Sgarro, 2017a,b), but also in the investigation of the history of texts.


The system was designed especially for analyzing longitudinal data from language acquisition studies.

the entries in the merged lexicon are sorted alphabetically.


The "Merge worksheets" macro is a general utility macro that integrates the contents of multiple spreadsheets in a file.

queries over single-item sequences (e.g., [Verb,SG]) calculate the number of tokens and types and can also return a list of items that matched the query (Figure 12).


The first stage is marked by the acquisition of the first 10 words, the second by a total lexicon size of 50 words, and then an additional 50 words for every subsequent stage (Adam and Bat-El, 2009).

for example, it is possible to get all verbs attempted by a child at a given age/lexical stage or range of ages/lexical stages.


The scope of queries can be constrained by age or stage of lexical development.

such an approach can help detecting problems in a limited part of the corpus.


3 The transcription procedures ("macros") operate on a vector of words.

each entry in the table of phono-orthographic groups has two fields (Table 2): the name of the group, and its members.


Besides this algorithm, we outline a machine learning approach to classify <EDT-Q, EDT-A> pair as correct or incorrect.

we demonstrate that involvement of background knowledge via virtual DTs for complex convergent questions requiring entailment is significant.


The main dialogue can be viewed as a one in the meta-level, and the object-level dialogue is naturally embedded into the meta-level one.

elaboration (LeftToRight) attribution (RightToLeft) TEXT: , the Investigative Committee of the Russian Federation believes elaboration (LeftToRight) TEXT: that the plane was hit by a missile from the air <where was it produced?> TEXT: which was not produced in Russia .


It is worth to take into consideration that the Polish language is more complex than English from the statistical point of view due to the rich morphology and a weekly constrained word order.

as the maximum length of a single tweet is 140 characters, so we made the representation of a single tweet to be a matrix of 140 code vectors 2 .


Firstly, in all cases the BPE-based embedding model built on the large corpus appeared to be superior to the domain-based model.

we replaced all occurrences of extralinguistic elements with symbols representing their types: • hashtags were exchanged with '#' sign, • mentions with the '@' sign, • URLs with the 'ň' sign (not present in neither Tweet Corpus nor Influencer Set).


(10), leading to the final objective function as follows.

word embedding is to convert symbolic representation of words to vector representation with semantic and syntactic meanings, which reflects the relations between words.


Recently, several approaches have been proposed to make more efficient word embedding matrices, usually based on contextual information (Søgaard et al., 2017;Choi et al., 2017).

while many methods have been proposed to learn more efficient representation, knowledge distillation from pretrained deep networks suggest that we can use more information from the soft target probability to train other neural networks.


5for all classes, but they are not available explicitly.

cBOW predicts a word given its neighbor words, and Skip-gram predicts the neighbor words given a word.


(baseline) I have not been right to realise that I am so old.

the other class probabilities can contain additional information describing the input data samples differently even when the samples are in the same class.


The figure shows the same tendency of improvements regardless of the change in dimension size.

if it is not the case, this might weaken the quality of the word's embedding vector.


The rarity of co-occurring every candidate word pair which possibly involves in a semantic relation leads us to exploit a method which does not necessarily need to see the word pair in a context together.

according to the distributional hypothesis which states "words that occur in similar contexts tend to have similar meanings" (Harris, 1954), distributional approaches try to recognize the relation between words based on their separate occurrence in the corpus which can be represented for example by their word embedding vectors (Mikolov et al.,2013) and these methods have shown great performance (Baroni et al., 2012;Turney and Pantel, 2010;Roller et al., 2014).


This task helps customers to absorb better a large number of comments and reviews before making decisions as well as producers to keep track of what customers think about their products (Liu, 2012).

in the scope of this paper, we focus on discussing neural-based systems for generic and opinions summarization.


We choose the optimal values of hyper-parameters in our model and baselines via a grid search on 30% of LAPTOP domain.

for a large corpus or multiple systems comparison, this test requires a huge amount of human effort.


These differences are statistically significant with the sign test (p < 0.05).

(2017), neural network-based approaches suffer the difficulties in achieving high performance on out-ofdomain data due to its high capability to fit indomain data.


We used the size of 100 for hidden layers in the LSTMs and 300 for word embeddings, which were initialized with pre-trained embeddings, word2vec-slim.

the labels are strongly affected by the length of reference summaries in the Dai-lyMail dataset.


To effectively avoid the influence of parse errors and take advantage of the recent advances in neural network-based approaches, we propose a model that jointly learns the discourse tree structure of the source document and a scoring function for sentence extraction.

all the parameters are updated to reproduce the correct labels and edges appearing in the training data D. λ is a parameter to control the priority of the output labels or the edges given by an RST parser.


The main issue is that most of the words are strongly imbalanced in terms of their sense distribution, thus, due to the lack of required training data, the supervised approaches present a lower recall in all-words WSD setting.

we show that the lexical knowledge base with its expansions and the way they are exploited has a strong impact on wSD performance and the proposed method allows for the efficient utilisation of large lexical knowledge bases.


It should be noted, that increasing the hidden layers did not improve the validation accuracy in our experiments.

in an n-dimensional space, the angle between the similar words should be close to zero.


For fine tuning the rules, we randomly pick 140 out of the 205 positive sentences (roughly 70%), containing a total of 158 NPE instances.

this gives us a final precision of 78.79%, a recall of 63.41% and an F1-score 70.27%.


Such cases of ellipsis are called endophoric.

nominal Ellipsis or noun Phrase Ellipsis (nPE, henceforth) is a type of ellipsis in linguists wherein the sub-parts of a nominal projection are elided, with the remaining projection pronounced in the overt syntax.


Addition of only the feature that checks for prepositions immediately following the noun modifier results into an increase in F1-score by 13.73%.

for testing, it is important to use both positive and negative samples.


We use some features that are closely related to the screen and user names of accounts.

it should be noted here that the fact that we and other researchers are able to identify bots quite reliably clearly shows that these assumptions are not unfounded.


That means, the training objective of the systems remains unchanged: they are required to correctly predict the value of the binary label at the first annotation layer.

these comparisons, however, fall outside of the scope of this paper.


While lexical similarity is an important factor in linguistic variation, we would argue that it does not capture all the translationally relevant features of texts.

reducing a functional vector to just the strongest component would be unfair to the functionally hybrid texts that fall under the genre labels of non-academic and editorial in our BNC slice.


• Spsim option 1 and Spsim option 2 are the only metrics which require supervised training, in order to learn grapheme mappings between language pairs (Gomes and Pereira Lopes, 2011).


A disadvantage of using text-formatted pretrained embeddings is that we could not generate embeddings for all words in the gold standard list.

(2011) design a new similarity metric that is able to learn spelling differences across languages.


The remainder of this paper is organized as follows.

predictive models directly try to predict a word from its neighbors in terms of learned dense embedding vectors (Baroni et al., 2014).


In the argument extraction step, for each marked predicate, we try to detect its arguments within a sentence by analyzing its syntax dependency tree with a number of manually constructed rules.

in this case, the model based on RuBERT shows worse performance than all other approaches.


However, for many predicates there are just few examples, and some semantic roles are also rare.

in this work, we additionally suggest using embeddings generated by deep pretrained language models, train models on automatically generated linguistic annotations (morphology / syntactic trees), and provide the full pipeline for semantic role labeling including argument extraction.


Designing such a measure and its automatisation, if at all achievable, is beyond the scope of this work.

{whip:4} incorporates the peripheral FE Instrument ('whip') of {strike:1} in the frame Cause harm assigned to both; -Reducing the scope of the frame through imposing more strict selectional restrictions on the FEs, e.g.


The first one is Using (Is Used by ↔ Uses), a frame-to-frame relation defined as a relationship between two frames where the first one makes reference in a very general kind of way to the structure of a more abstract, schematic frame (Ruppenhofer et al., 2016, p. 83); it may be viewed as a kind of weak inheritance (Petruck, 2015) where only some of the FEs in the parent frame have a corresponding entity in the child frame, and if such exist, they are more specific (Petruck and de Melo, 2012).

we separate: (i) suggested frames related to the one assigned from the hypernym, which are given higher priority; (ii) unrelated suggestions.


Moreover, such features significantly contributed to generalize the proposed RE to other domains of interest.

to conclude, the overall achieved results suggest that more accurate semantic information about entity instances can contribute a great deal to RE.


In addition, we collected three sets of reference data as negative samples using the hashtag #realfood #comidareal and #fitness.

different approaches to text and data mining methods can be applicable to social media data and may prove invaluable for health moni-toring and surveillance.


In addition, the study shows that Twitter is the major data source from social networks and English is the main language studied in the different papers.

in this subsection, we report and discuss the performances of our systems on the Spanish anorexic   Table 5: Statistics about positive and negative words in the corpus.


On the one hand, if we look at the false positives, we can see that two of the reasons why our system can be wrong is because it detects that there are words related to food and also that the vocabulary of the other tweets labeled as control is very similar to the vocabulary used in anorexia.

in addition, this is one of the few papers which center on languages other than English.


In this section, we describe different experiments we carried out to test the validity of the SAD corpus.


Today, they have millions more pages and loyal followers, and the Internet has connected thousands of people with eating disorders.

the treatment of this information using NLP technologies can be applied to the automatic detection of mental problems such as eating disorders.


Another difference is CCG's (by default limited) support for word order.

since word order can label (now already semantic) constituents, as in John sUB loves M ary OBJ , a limited form of syntax-semantics interface is also expected.


EnetCollect 1  aims at exploring a solution to this challenge by combining the activities performed in language learning with approaches for crowdsourcing language-related datasets.

for each of these terms, we acquired more than 20 terms that are related to them.


V-trel can be accessed through two interfaces, a Tele-gram chatbot and a Web application.

any crowdsourcing ambition is faced with the challenge of attracting and retaining crowdworkers and with safeguarding the quality of results produced by the crowd (Daniel et al., 2018).


For in-stance, the embeddings for sub (prefix, mid-word), est (whole-word, suffix), ion (suffix, whole-word), mid (prefix, suffix), and the (whole-word, suffix) lie far from each other.

the figure clearly shows that different forms of the same character n-gram have different contributions towards the likability prediction of books.


Each action contains the information on who performed it and what values the agent provided.

for our experiments, each of these states handles one particular slot.


We now list the domain intents and their descriptions as follows: Making a Transfer.

the context also contains a form of turn that the agent has never seen during training, which is the first turn compression of the second example in Table 10.


According to official test sets' result, their model ranked first in phraselevel and second in message-level tasks.

recently, deep learning is widely used to classify the text as it is capa-ble of extracting public opinion regarding a specific topic and also works excellently with highlevel features.


Also called frequency based, it provides a sparse representation of corpus of documents as a matrix.

while considering the highest F1 score, we got 93.33 percent from the combination of tf-idf vectorizer and unigram model.


English language has the luxury of having numerous annotated datasets as well as having well-tuned text preprocessing techniques.

probability of collision should be considered when choosing the size.


It is fundamental to take into account the contextual information in order to better distribute the words into the classes.

indeed, there are words with many forms.


In fact, a semantic class may correspond to a label or a group of labels, whereas a label cannot belong to only one class.

evaluating the constructed model by calculating its perplexity.


In this work, we adopted the full diacritization level, at which all diacritics should be specified in a word.

the Tunisian dialect is the current mother tongue and the spoken language of all Tunisians from different origins and distinct social belongings.


To achieve better context-sensitive sourcetarget mappings, traditional SMT systems rely on phrase-level translation models.

the success of this kind of systems highly depends on the dictionary coverage.


• IBM Watson Natural Language Understanding.

recent years have witnessed an increased impetus on machine learning methods for data-driven Fr detection (Mukherjee et al., 2013;Ott et al., 2011;rout et al., 2017) The performance of machine learning models for detecting Fr is heavily influenced by the data representation (or features) in their application (Bengio et al., 2013).


Intuitively, finegrained classifications are often considered more error-prone than those with a small number of class values.

providing highquality sources might lower the attention and interest of the annotators.


The results show that the model GRU+SVM with ELMo yields the best performance of 73.03% on the development data, while the model GRU+SVM with ELMo+POS outperforms all the other models on the test dataset with a micro-average F1 score of 69.93%, by being marginally better than GRU+SVM with ELMo 7 .

equations 4 and 5 show the mechanisms used to calculate the weights, where w corresponds to the weights in the single-layer neural network, and ν t is the single value, which is the result of feeding y t to the fully-connected layer.


Let n +− t denote for the time step t, the union of positive and negative examples.

the positive impact of the backward external initialisation increases with the volume of data.


we added a regularisation term to their loss using the Hardshrink activation function, successfully getting longer distribution tails for the drifts.


In (B), the string " " has only one grammatical analysis result " " (Nanjin).

in STEP (1) at the line 14 of Algorithm 1, when an identical surface was associated with two or Figure 1: Example of a lattice that is built by our baseline method and our proposed model using a CiS dictionary.


Finally, in Section 5, conclusions are drawn and future developments outlined.

furthermore, we thank the reviewers for their valuable comments.


This means that the slightly higher entropy is found in the language pair where there is slightly lower intelligibility.

the character adaptation surprisal values between language A and language B are not necessarily the same as between language B and language A.


According to Gatt and Krahmer (2017), there has been a plenty of works which investigated the generation of NL texts from Semantic Web Technologies (SWT) as an input data (Cimiano et al., 2013;Duma and Klein, 2013;Ell and Harth, 2014;Biran and McKeown, 2015).

as expected, the verbalization of short expressions leads to sentences which read as if they have been generated by a human.


we get everything that is a city and located in France.

we will consider the used of attention-based encoder-decoder networks to improve the fluency of complex sentences.


We see a similar pattern for both extractive and abstractive summarization, with an increase of deletion for longer summaries produced by the extractive system.

our denoising methods are currently better suited for extractive than for absctractive summarization.


democratic to republican) or gender (e.g.

importantly, these methods assume that some parallel training data is already available, which impedes their application in settings where there is no parallel data whatsoever, which is the case for many text rewriting tasks such as style transfer.


A quick look at an image is sufficient for a human to say a few words related to that image.


With increasing k, more concept clusters of unused single terms are added.

the feature combinations are formed by concatenating the feature spaces of each method.


The feature combinations are formed by concatenating the feature spaces of each method.

largely balanced validation sets are formed with data from various sources.


Among the false negatives we analyzed, we found that the model is most likely to miss "tricky" quotations that are unusual in their grammatical structure.

the results in table 1 show that NQD cannot beat the performance of Scheible et al.


Next, we carry out more methods to clean the texts.

the evaluation was carried out in accordance with parameters set out for SemEval-2019 task 12, featuring strict and overlap categories on macro and micro levels.


On the one hand large-scale geographical databases, such as GeoNames 1 , make information about many different locations easily and freely available.

while a lot more research has been focused on these kind of architectures, we hope to explore tasks other than only sequence labelling.


automatic post-editing (APE) (Junczys-Dowmunt and Grundkiewicz, 2016;.

it could then be questioned whether the extra training effort in itself does not partly explain the positive effect of back-translation.


We demonstrate the results of our model in section five.

we accomplish this by incorporating information about part-of-speech (PoS) tags into the model.


The graph-based embeddings are somewhat better than those based solely on the FrameNet annotated corpus; the difference is especially pronounced in the evaluations without the lexicon.

the Wikipedia data is approximately the same size as the pseudo-corpus.


As described above in Section 1, FrameNet has a structure which connects frames via different semantic relations.

their frame elements are more likely to appear across different domains.


We have constructed such a model following the methodology described in Goikoetxea et al.

the paper is structured as follows: the next section presents related work; section 3 deals with some preliminary experiments aimed at replicating previous work described in the literature, but in a slightly modified setting; section 4 outlines several strategies for improving the structure of the knowledge base used for KBWSD; the penultimate section reports on our core experimental work, and the final section concludes the paper.


The dependency analyses of the sentences are not part of the original annotation of SemCor.

in the case of the preliminary experiments, we have used a synset embedding model constructed via random walks along the WordNet KB 7 .


the conjunction ambiguity is related more to fluency than it is to adequacy.


The opposite applies to the Czech parties SPD, Rozumní, Polish party Porozumienie and Slovak party L'S-HZDS.

we set a baseline which uses standard machine learning approach, and set an upper bound which uses manually created external knowledge.


As an example the word 'bank' Sentence Pair Similarity 1.

contextualised word embeddings perform better than standard word embeddings in many natural language processing tasks like question answering, textual entailment etc.


Each pair is annotated with a relatedness score between [1,5] corresponding to the average relatedness judged by 10 different individuals.

siamese networks are popular among tasks that involve finding similarity or a relationship between two comparable things.


In Figure 1, only terms with a probability higher than 50% (up until rank 1352) were labelled as terms by HAMLET.

those that are validated with EWN as medical terms.


RSR -RuSentRel based dataset with sentence-level attitude labeling (Section 3.1); 3.

this process involves the tokenization to demarcate text string into words and punctuation signs.


The list of authorized objects is necessary to avoid accidental misses from the NER model.

all the entities appeared between pair endings should be authorized objects; in a specific sentence, the supposed relation between countries can be false.


Improving over the matrix factorisation and the edge reconstruction approaches, the random walk technique is effective and accommodates global information on the nodes.

the cell A Gij contains the associative strength of word i with word j, obtained from the frequency with which word j is responded when word i is cued.


One notes that all measures are higher than those obtained when using the GOLDPERS (see rows PERS_1 in Table 4), which can partly be due to the inclusion of first names only into the present version of SRPNER.

to the best of our knowledge, StANFORD NER and SPACY NER were used for the first time for the recognition of personal names in Serbian texts.


Initial results suggest that both tasks may be performed with relatively high accuracy by making use of simple models based on char n-grams and feature selection.

subsequent to semEval-2016, a number of improved systems have been proposed.


For the stance recognition task, a range of n-gram models -from 1 to 5 words and from 3 to 16 characters -was considered, and we found that character-based models always outperform wordbased models.

the work in (Zarrella and Marsh, 2016) presents the best overall performance in the SemEval-2016 shared task (Mohammad et al., 2016b) on supervised stance recognition.


Recall the definition of pVCC: stays increase completeness, and jumps increase cleanness.

2), but stating the principle of "jump and stay" is much more clearer.


If the jump would omit the last filler from a VCC, we do not take this step.

the basic processing unit is the clause, the unit which contains a verb together with its complements and adjuncts, and consequently, a pVCC.


In fact, we do not do a jump in every case.

this paper is 1. a proof of concept concerning the corpus lattice model, opening the way to investigate this structure further through our implementation; and 2. a proof of concept of the "jump and stay" idea and the algorithm itself, opening the way to apply it further, e.g.


In Table 2 we provide an individual example of a linear conversation from our dataset including offensive probabilities.

do you find your government to be trustworthy?


Leaf nodes are posts which have no further direct replies.

finally, in Section 3.3, we show how we intend to further analyse these linear dialogues by applying decoupling functions to model the change of offensive probability.


It appears  with a broad range of other emojis with a relatively high frequency.

it is often remarked that they lack interpretability, in the sense that individual values in such vectors do not carry any easily interpretable inherent significance.


In this way, the system learns to detect and use more useful features.

we then concatenated the vector to the rest of the word embeddings.


While we can still build a stable system based on this data, the class imbalance makes our model more vulnerable to overfitting.

in the last years, the interest in NER for Slavic languages grew.


Another place for improvements would be to distinguish better between B-LOC and B-ORG, as many places and organization have identical names, or at least the identical first words.

we show that named entity recognition needs only coarse-grained POS tags, but at the same time it can benefit from simultaneously using some POS information of different granularity.


Traditionally, NER has focused on recognizing entities such as person (PER), organization (ORG), location (LOC), and miscellaneous (MISC).

we created the input vectors for the tokens in the sentences as a concatenation of three vectors: a word embedding vector, a character embedding vector, and a vector containing some grammatical features, called a grammatical vector.


At our disposal there are 12 lists (see Table 2 for more detailed information).

a system extracting relevant cybersecurity information from unstructured publications could be of great use for a cybersecurity expert.


In addition, to fine-tune and evaluate models in experiment 2 (see section 5.4), we sample smallscale sets with a higher proportion (1%) of cognates, presented in Table 3, SAMI-FT and SAMI-FT-TEST.

we implemented the model using the Keras library with Tensorflow backend 4 .


A straightforward example is the Italian-Spanish pair (notte, noche), with a similar form and common meaning.

our approach consists of learning a similarity metric from example cognates in Indo-European languages and applying it to low-resource Sami languages of the Uralic family.


In order to determine how common each word is, we used pre-trained frequency lists in all languages (Michel et al., 2010).

figure 3: POS-MTUs, English-German thereby determining the source and the target sentences.


As one realization of this hypothesis, we assume that translations would use more common, frequent words than originals.

the classification unit used in all the above-mentioned research was larger chunks of text, typically 2,000 tokens.


This is not unexpected, since the English data set is larger than the German one.

abusive language detection has received much attention in the last years, and recent approaches perform the task in a number of different languages.


This paper presents a comparison of different techniques for solving the task of company industry classification based on textual descriptions of companies in DBpedia 1 .

other methods with comparable results are Universal Language Model Fine-tuning (ULMFiT), (Howard and Ruder, 2018) with error 0.8 for DBpedia dataset.


NLP pipeline for stopword removal and stemming, resulting text is processed into unigrams) but instead of one-hot vectors, GloVe vector embeddings are used.

the main advantage of transformer-XL  is that it allows the capture of longer-term dependencies and resolves context fragmentation problem.


The Industries classification is based on the nature of organization's activity and is generated from the industry 8 property of DBPedia.

the 300dimensional GloVe vectors trained on the large Common Crawl corpus of 840 billion tokens with a vocabulary of 2.2 million words.


Even if some studies try to find connections by QPT and brain functionality at neural level (Khrennikov et al., 2018), the use of QPT in this field is simply as an explanation theory useful to model real phenomena in the right way, but none of them is really claiming that our brain is working by applying QPT axioms.

the large set of works introducing word and sentence embeddings (Mikolov et al., 2013;Pennington et al., 2014;Bojanowski et al., 2016;Le and Mikolov, 2014;Sutskever et al., 2014;Kiros et al., 2015;Cer et al., 2018) produce representations in the real domain while we need similar vectors but in the complex domain.


We can see that the qualities of neighbors in two embedding matrices are close.

we conduct both qualitative and quantitative evaluations of the embeddings from Transformers and Trans-noEnc models.


An examples, consider attributes such as 'disputed territories' from the country domain, or 'supplier' from the organization domain; arguably the values of these attributes is so specific that numeric information cannot help.

in this paper, we propose a simple feedforward neural architecture to jointly predict numeric and categorical attributes based on embeddings learned from textual occurrences of the entities in question.


Conversely, results for numeric prediction improve when the model pays more attention to these attributes, for low values of α (recall that lower NRS values are better).

each unit in the output layer corresponds to one numeric attribute, and the model predicts all numeric attributes simultaneously.


Wrong negation: Classifying negated sentiment words accurately requires more effort than negating sentiment words that are preceded by a negator.

we applied Fleiss' Kappa (Fleiss, 1971) to measure the agreement among the annotators scoring a substantial agreement of 0.74 (Landis and Koch, 1977).


We tried our best to keep three annotators per task, but in a few cases we had one annotator at hand.

the annotations carried out to create SenZi and the datasets took place at different times between 2016 and 2018.


This contrasts with our intuition of aligning the word vectors based on a few static (from a lexical semantic point of view) words.

this is because the alignments of the former are based on the representations of words that are indeed stable over time.


monolingual Impact Similar to the RCSLS, our model changes the cosine distance between word vectors in the same language, that is, it also has an impact on the monolingual embedding space.


Limitations Similar to the baseline models, the main limitation of our model is that it can not generate multi-word expressions such as phrases on the target side, although our model is able to represent a sequence of strings in the source encoder.

although the existing models achieve high performance on pairs of morphologically simple languages, they perform very poorly on morphologically rich languages such as Turkish and Finnish.


(2010b) looked for new claims on the Web that entail the ones that have already been collected.

other claim monitoring tools include FactWatcher (Hassan et al., 2014) and Dis-puteFinder (Ennals et al., 2010b).


We approach the task of check-worthiness prediction as a multi-source learning problem, using different sources of annotation over the same training dataset.

this kind of neural network architecture for multi-task learning is known in the literature as hard parameter sharing (Caruana, 1993), and it can greatly reduce the risk of overfitting.


Therefor we run 10 times 10-fold cross validation on random selected folds.

more generally, the result of the experiment provides some evidence that current NLP methods are quite cable to "understand" the meaning of text at an almost human level.


In addition, we need to capture information that is relevant to the match, but is expressed in a semi-explicit or implicit way, such as health conditions, confidence, psyche, etc.

it then remains unchanged throughout the remaining experiments.


the annotation process was a collective effort and a number of data annotators were involved in this step.


Results in Table 6 show a slight improvement over the InIt-based scoring, but the difference is not as high as we expected.


Coreference resolution and sentence fusion may help to lower the degree of redundancy introduced through the syntactic sentence simplification.

aMR graphs also rely on PropBank framework whose limitations pose additional constraints on aMR graphs.


(2013) used accuracy metric to measure the quality of word embeddings on the task in which only when the expected word is on top of the prediction list, then the model gets +1 for true positive count.

unlike the side-by-side visualization, this interactive visualization can only visualize up to a certain amount of embedding vectors as long as the tensor graph is less than 2GB.


• Evaluator evaluates the pre-trained embeddings for a downstream task.

among many embeddings at different learning steps of dpUGC, how to choose a suitable embedding to achieve a good trade-off between data privacy and data utility is a key challenge.


An important challenge is handling of the segmentation in a correct way or applying a more advanced normalisation process before tagging.

we proposed significant expansions to the state-of-the-art tagger for Polish, namely Toygger, that resulted in large gain in per-Form Tag W prep:loc:nwok kredytowaniu ger:sg:loc:n:imperf:aff zakupu subst:sg:gen:m3 auta subst:sg:gen:n poszło praet:sg:n:perf bardzo adv:pos sprawnie adv:pos i conj całkiem adv przyzwoite adj:pl:nom:f:pos spłaty subst:pl:nom:f .


And the second, based on clustering of word embeddings into a predefined number of groups and using centroids as elements of final document vectors.

the embeddings are sentence aware and could solve a problem of polysemous words (words with multiple meanings).


If there are no terms with the same document frequency in an ordered term list, the index of each term can be reasonably considered as its rank in this corpus.

for different corpora, the order of terms will be different, as well as the index of each term.


The current state of the art for First Story Detection (FSD) are nearest neighbourbased models with traditional term vector representations; however, one challenge faced by FSD models is that the document representation is usually defined by the vocabulary and term frequency from a background corpus.

we first look at the comparisons between corpora before looking at FSD performance for different background corpora.


The state of the art document representation model for P2P FSD models remains the traditional term vector models, due, in part, to their specificity of terms (Wang et al., 2018).

given these two factors cannot always be mutually satisfied, in this paper we examine whether the distributional similarity of common terms is more important than the scale of common terms for FSD.


Our results show that term distributional similarity is more predictive of good FSD performance than the scale of common terms; and, thus we demonstrate that a smaller recent domain-related corpus will be more suitable than a very largescale general corpus for FSD.

the ideal background corpus for FSD should be both large-scale and similar in frequency distribution to the assumed target corpus.


The motivation for a universal classifier might not be obvious at first, as clearly the best performance is achieved by in-domain classification (Twitter may need tweet classifier, Facebook needs posts classifier, IMDB needs review classifier, and so on).

in this experiment we merged all sub-parts of the sentiment treebank (jun18, neg, polevaltest, rev, sklad) into one data set presented as TW.


The analysis only considers NIL entities that were marked as such (e.g., NIL or similar designation).

for example, if a KB has more name variants (e.g., Bobby Kennedy and RfK for Robert f. Kennedy) than the corpus annotators have considered, NEL systems able to correctly detect these name variants will be penalized since they do not occur in the corpus and are, therefore, considered errors.


Counts from rows (i) and (ii) were taken directly from the corpora; row (iii) count was estimated based on SPARQL queries that aim at linking NIL entities to the KB; and counts for columns (iv) and (v) were estimated based on annotating samples from each data set.

reuse of old gold standards can lead to problematic results (e.g., entities declared NIL in the gold can currently exist in the current KB version and can be retrieved by annotator tools) or even unfair evaluations (e.g., tools that use an old KB should not be compared with those who use the latest updates).


Since edges do not have a pre-specified order, we propose a set-based learning method.

this model assumes conditional independence of the edges.


for instance, recent technological advances make the provision of various eHealth services feasible.


For a QA application, it means that the classes of domain-specific semantic concepts can be used to generate signature vectors and the semantic similarity with the signature vectors of the answer candidates can be computed for retrieval and ranking.

we are interested in training new neural networks in multi-and cross-lingual term extraction and definition retrieval settings.


The aim of the survey was to identify whether the group with autism: i) experienced any barriers when reading online reviews, ii) what these potential barriers were, and iii) what automatic methods would be best suited to improve the accessibility of online reviews for people with autism.

nine control participants said they read reviews Sometimes (27.27%, n = Figure 1: In general, do you find understanding product reviews: 9) and one participant selected the option Rarely (3.03%, n = 1).


For example, one of the most authoritative sources of such guidelines for people with cognitive disabilities, the European Guidelines for the Production of Easy-to-Read Information (Freyhoff et al., 1998), lists requirements that fit the profile of people with moderate to severe comprehension deficits, but not those of more highly able individuals.

very little is known about the perceptions of adults with high-functioning autism on the usefulness of specific simplification strategies.


There are, in general, two major kinds of allomorphy with respect to their source: (i) phonologically conditioned, and (ii) morphologically or lexically conditioned.

words, or rather morphemes, that are affected by this process have a voiced obstruent in the final position in their UR (as in Table 6(a)), which gets devoiced unless a vowel-initial suffix follows.


We also experimented with using the most frequent 10000, 20000, and 25000 words, however, the results in the crossvalidation experiments were lower.

(2018) offered a new word embedding representation model named as ELMo.


(2010) extracted different features from short-texts and used these with Naive Bayes to classify them.

pointing to the weaknesses of the BoW approach, different kernels have been developed for SVM such as semantic kernels that use TF-IDF (Salton & Buckley, 1988) and its variants that apply different term weighting functions on the term incidence matrix.


Naive Bayes, Random Forest, and SVM obtain better scores when morphological analysis is performed.

the grammatical and syntactic features of the turkish language pose additional challenges for short-text classification.


They are obtained by using the bidirectional approach with masked language model (Taylor, 1953) in training.

in spite of these improvements, the proposed Transformer Encoder model achieves the highest scores in all metrics for tweet classification even though it does not use the morphological analysis.


.⊗V j ⊗V k and U be a tensor in V k ⊗V m ⊗.

distributional semantic models, best summarised by the dictum of Firth (1957) that "You shall know a word by the company it keeps," provide an elegant and tractable way of learning semantic representations of words from text.


The average combiner with variable binding is the most memory intensive since the number of arguments of the result() predicate can become large (there is an argument for each individual and event in the sentence).

table 2 summarizes the results of our experiments.


We present an approach to training coarse to fine grained sense disambiguation systems in the presence of such annotation inconsistencies.

the proposed framework of learning with positive and unlabeled examples for sense disambiguation could be applied on the entire Wikipedia without any manual annotations.


Table 2 shows the results of the CORE task, with runs listed in alphabetical order.

hence the task is about comparing pairs of items.


Our first run uses an align-and-penalize algorithm, which extends the second approach by giving penalties to the words that are poorly aligned.

related words can have similarity scores as high as what similar words get, as illustrated by "doctor" and "hospital" in Table 1.


We also added to it more than 2,000 verb phrases extracted from WordNet.

a context window of ±4 words allows us to compute semantic similarity between words with different POS.


Our approach treats text pairs as structural objects which provides much richer representation for the learning algorithm to extract useful patterns.

the total number of takelab's features is 21.


The same setting of (Croce et al., 2012a) has been adopted for the space acquisition.

specific phrases are filtered according to linguistic policies, e.g.


• Preceding or following lemma (or word form) content word appearing in the same sentence as the target word.

the recall is low, in almost all cases less than a third of nouns in one list appear in the other.


OnWN was trained on MSRpar train with LK and DK.

this makes it sensitive to the choice of training data, which ideally would have similar characteristics to the individual kernels, as well as a similar gold standard distribution to the test data.


While the evaluation measure, Pearson correlation, does not take into account the shape of the output distribution, Figure 1 shows that this information may be a useful indicator of model quality and behaviour.

(2011) and from there derive two dependencybased methods.


These distributions were estimated via crowdsourcing.

the system includes various length-related features, where , and length(x) denotes the number of tokens in x. log denotes the natural logarithm.


A number of similarity metrics were proposed under either the attributional similarity (Turney, 2006) or the maximum sense similarity (Resnik, 1995) assumptions of lexical semantics 1 .

text semantic similarity estimation has been an active research area, thanks to a variety of potential applications and the wide availability of data afforded by the world wide web.


In our previous participation in SemEval12-STS task (Malandrakis et al., 2012) we employed a modification of the pointwise mutual information based on the maximum sense similarity assumption (Resnik, 1995) and the minimization of the respective error in similarity estimation.

we express containment as the amount of ngrams of a sentence contained in another.


We calculated the distance to align each group with every pair of aligned verbs.

(3) "The two sentences are roughly equivalent, but some important information differs/missing".


On a smaller scale word sense disambiguation, semantic role labeling and time and date resolution.

the tables have to be created only with the similar groups of the sentences.


Given two short texts or sentences s 1 and s 2 , we denote the word set of s 1 and s 2 as S 1 and S 2 , the length (i.e., number of words) of s 1 and s 2 as |S 1 | and |S 2 |.

we create 12 string based features in consideration of the common sequence shared by two texts.


The typed-similarity dataset comprises pairs of Cultural Heritage items from Europeana 1 , a single access point to digitised versions of books, paintings, films, museum objects and archival records from institutions throughout Europe.

the similarity between two vectors of words can thus be implemented as the similarity between the probability distributions, as given by the cosine between the vectors.


We assume that the year of creation or the year denoting when the event took place in an artefact are good indicators for time similarity.

for example the vectors of two articles x and y are: where x and y are two Wikipedia articles and x → l i is a link from article x to article l i .


Given the similarity shifts in the different datasets (cf.

the results for the cross-validation process are summarized in table 2.


The second and third best-performing measures were WordNet similarity and Levenshtein's edit distance.

in this paper we introduced the LiPN-CORE system, which combines semantic, syntactic an lexical measures of text similarity in a linear regression model.


First of all, sentences p and q are analysed in order to extract all the included WordNet synsets.

worst performing similarity measures were Named Entity Overlap, Syntactic Dependencies and ESA.


We decided to omit the Microsoft Research Paraphrase Corpus (MSRpar and MSRvid) because we felt that the types of sentence pairs in this corpus were too different from the development data.

overall, our choice of ridge regression is justified.


Combining all the available features indeed results in the highest mean score.

column maxima (wp500-long), respectively.


angle) between the first sentence of a given pair and the second sentences of all word pairs in the same data set.

we tested linear regression, regularized linear regression (ridge regression), Bayesian ridge regression, support vector regression and regression trees.


For example, the longest common substring between the following sentences is bolded: A woman and man are dancing in the rain.

two short texts are considered similar if they both convey similar messages.


Text similarity has also been used for relevance feedback and text classification (Rocchio, 1971), word sense disambiguation (Lesk, 1986;Schutze, 1998), and extractive summarization , in the automatic evaluation of machine translation (Papineni et al., 2002), text summarization (Lin and Hovy, 2003), text coherence (Lapata and Barzilay, 2005) and in plagiarism detection (Nawab et al., 2011).

briefly, for each open-class word in one of the input texts, we compute the maximum semantic similarity 4 that can be obtained by pairing it with any open-class word in the other input text.


triarcs (1.87B items) consist of four content words (example in Figure 3f).

see Figure 1 for an example of a syntactic-ngram.


The syntactic representation we work with is based on dependencygrammar.

to keep the  data manageable, we employ a frequency threshold of 10 on the corpus-level count.


The assumption behind these methods is that noncompositional MWEs are more syntactically fixed than compositional MWEs.

6 As we are ideally after broad coverage over multiple languages and MWEs/component words in a given language, we exclude Babelnet and Wiktionary from our current research.


3 They computed backward and forward entropy to try to remedy the problem with especially high-frequency phrases.

for example, with ad hoc, the fact that neither ad nor hoc are standalone English words, makes ad hoc a lexicallyidiosyncratic MWE; with shoot the breeze, on the other hand, we have semantic idiosyncrasy, as the meaning of "to chat" in usages such as It was good to shoot the breeze with you 1 cannot be predicted from the meanings of the component words shoot and breeze.


One can see from the table that when the metaphorical verb stir in "stir excitement" is paraphrased as the literal "provoke", the subsequent paraphrasing of "provoke" does not produce "stir".

the baseline system is the implementation of the selectional preference violation view of Wilks (1978) using automatically induced SPs.


The system P = 0.68 and R = 0.66, whereas the baseline only attains P = 0.17 and R = 0.55.

cognitive evidence suggests that humans are likely to perform identification and interpretation simultaneously, as part of a holistic metaphor comprehension process (Coulson, 2008;Utsumi, 2011;Gibbs and Colston, 2012).


Second, in a small number of cases, both annotators agree on one member of the set of alternatives.

the 76% accuracy reached using the simple textbased classifier suggests that a system which has teachers supply source sentences instead of target answers and then automatically aligns learner answers to the text, while nowhere near comparable to the state-of-the-art supervised system, still achieves a reasonably accurate classification.


Kramer 2010proposed the "Gross National Happiness" index and Kivran-Swaine and Naaman (2011) examined associations between user expressions of positive and negative emotions and the size and density of social networks.

to do this, we first judged a sample of 1,000 instances of LIWC terms occurring in Facebook posts to indicate whether they contribute signal towards the associated LIWC category (i.e.


We argue that more data of this kind would be helpful to improve existing approaches to linking implicit arguments in discourse and to enable more in-depth studies of the phenomenon itself.

their heuristically created training data might not represent implicit argument instances adequately.


The counts for these categories appear in Table 6.

these features are often quite sparse and do not generalize well.


Both rely on Wikipedia links and are based on frequencies of these links.

possible senses are given by WordNet and the authors report an inter-annotator agreement of .93 for the RG dataset.


We performed a second evaluation study where we asked three human annotators 12 to rate the similarity of word-level pairs in the dataset by Rubenstein and Goodenough (1965).

our novel measure ESA on senses provides the best results.


Links in Wiktionary articles are disambiguated and thus transform the resource to a sense-based resource.

to our work, the text itself is not changed and similarity is computed on the level of texts.


Word pairs are constructed by adding one word with two clearly distinct senses and a second word, which has a high similarity to only one of the senses.

in this paper we investigate whether similarity should be measured on the sense level.


Figure 1 (a)) unknown cells can be assigned {1, 4, 7, 8}.

if  The shortest paths cup#1→handle#1→golf_club#2 and cup#7→golf#1→golf_club#2 only exist because the sense golf_club#2 (anchored to the more polysemous lemma club) is present, if it was not then the SENSE_SHIFTS filter would have removed these alternative senses.


WordNet-based context enrichment uses the Word-Net synonyms to obtain the context, and concatenates them into the given word to build the ESA vector.

for instance, there is a given word pair "train and car", car has 8 different synsets that build 8 different contexts, and train has 6 different synsets that build 6 different contexts.


freely distributed by ELDA for research purposes 3 (Catalog Reference ELRA-W0063).

if we compare these results with the paradigmatic relations encoded in DiCoEnviro, we see that, in the case of absorb, 3 of its neighbours are encoded in the dictionary, and all 3 are antonyms or terms having opposite meanings: emit, radiate, and reflect.


If the verb is negated (the left sibling of the I Will subtree spans exactly the word not), we add the postfix NOT to the verb feature, for example verb=quit NOT.

we follow the pairwise approach to ranking (Herbrich et al., 1999;Cao et al., 2007) that reduces ranking to a binary classification problem.


In order to reduce the complexity of the task, we based our analysis on the subset of categories, for which the category text described a single problem (a single H, speaking in RTE terms).

in our analysis, we manually compared the email texts to the descriptions of their associated categories in order to investigate the nature of the inference steps involved.